{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n.. redirect-from:: /tutorials/introductory/pyplot\n\n\n# Pyplot tutorial\n\nAn introduction to the pyplot interface.  Please also see\n`quick_start` for an overview of how Matplotlib\nworks and `api_interfaces` for an explanation of the trade-offs between the\nsupported user APIs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to pyplot\n\n:mod:`matplotlib.pyplot` is a collection of functions that make matplotlib\nwork like MATLAB.  Each ``pyplot`` function makes some change to a figure:\ne.g., creates a figure, creates a plotting area in a figure, plots some lines\nin a plotting area, decorates the plot with labels, etc.\n\nIn :mod:`matplotlib.pyplot` various states are preserved\nacross function calls, so that it keeps track of things like\nthe current figure and plotting area, and the plotting\nfunctions are directed to the current Axes (please note that we use uppercase\nAxes to refer to the `~.axes.Axes` concept, which is a central\n`part of a figure <figure_parts>`\nand not only the plural of *axis*).\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The implicit pyplot API is generally less verbose but also not as flexible as the\n   explicit API.  Most of the function calls you see here can also be called\n   as methods from an ``Axes`` object. We recommend browsing the tutorials\n   and examples to see how this works. See `api_interfaces` for an\n   explanation of the trade-off of the supported user APIs.</p></div>\n\nGenerating visualizations with pyplot is very quick:\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:54.301560Z",
     "start_time": "2024-08-07T07:40:54.225557Z"
    }
   },
   "source": [
    "import matplotlib.pyplot as plt\n\nplt.plot([1, 2, 3, 4])\nplt.ylabel('some numbers')\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may be wondering why the x-axis ranges from 0-3 and the y-axis\nfrom 1-4.  If you provide a single list or array to\n`~.pyplot.plot`, matplotlib assumes it is a\nsequence of y values, and automatically generates the x values for\nyou.  Since python ranges start with 0, the default x vector has the\nsame length as y but starts with 0; therefore, the x data are\n``[0, 1, 2, 3]``.\n\n`~.pyplot.plot` is a versatile function, and will take an arbitrary number of\narguments.  For example, to plot x versus y, you can write:\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:54.383347Z",
     "start_time": "2024-08-07T07:40:54.308565Z"
    }
   },
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d8737b1a0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting the style of your plot\n\nFor every x, y pair of arguments, there is an optional third argument\nwhich is the format string that indicates the color and line type of\nthe plot.  The letters and symbols of the format string are from\nMATLAB, and you concatenate a color string with a line style string.\nThe default format string is 'b-', which is a solid blue line.  For\nexample, to plot the above with red circles, you would issue\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:54.483513Z",
     "start_time": "2024-08-07T07:40:54.418397Z"
    }
   },
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\nplt.axis((0, 6, 0, 20))\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the `~.pyplot.plot` documentation for a complete\nlist of line styles and format strings.  The\n`~.pyplot.axis` function in the example above takes a\nlist of ``[xmin, xmax, ymin, ymax]`` and specifies the viewport of the\nAxes.\n\nIf matplotlib were limited to working with lists, it would be fairly\nuseless for numeric processing.  Generally, you will use [numpy](https://numpy.org/) arrays.  In fact, all sequences are\nconverted to numpy arrays internally.  The example below illustrates\nplotting several lines with different format styles in one function call\nusing arrays.\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:54.561387Z",
     "start_time": "2024-08-07T07:40:54.496138Z"
    }
   },
   "source": [
    "import numpy as np\n\n# evenly sampled time at 200ms intervals\nt = np.arange(0., 5., 0.2)\n\n# red dashes, blue squares and green triangles\nplt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n## Plotting with keyword strings\n\nThere are some instances where you have data in a format that lets you\naccess particular variables with strings. For example, with `structured arrays`_\nor `pandas.DataFrame`.\n\n\nMatplotlib allows you to provide such an object with\nthe ``data`` keyword argument. If provided, then you may generate plots with\nthe strings corresponding to these variables.\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:54.633901Z",
     "start_time": "2024-08-07T07:40:54.567632Z"
    }
   },
   "source": [
    "data = {'a': np.arange(50),\n        'c': np.random.randint(0, 50, 50),\n        'd': np.random.randn(50)}\ndata['b'] = data['a'] + 10 * np.random.randn(50)\ndata['d'] = np.abs(data['d']) * 100\n\nplt.scatter('a', 'b', c='c', s='d', data=data)\nplt.xlabel('entry a')\nplt.ylabel('entry b')\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n## Plotting with categorical variables\n\nIt is also possible to create a plot using categorical variables.\nMatplotlib allows you to pass categorical variables directly to\nmany plotting functions. For example:\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:54.772382Z",
     "start_time": "2024-08-07T07:40:54.646025Z"
    }
   },
   "source": [
    "names = ['group_a', 'group_b', 'group_c']\nvalues = [1, 10, 100]\n\nplt.figure(figsize=(9, 3))\n\nplt.subplot(131)\nplt.bar(names, values)\nplt.subplot(132)\nplt.scatter(names, values)\nplt.subplot(133)\nplt.plot(names, values)\nplt.suptitle('Categorical Plotting')\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 900x300 with 3 Axes>"
      ],
      "image/png": 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9evTQ7Nmzq3x9xowZmjlzpmbPnq1169YpPj5egwcPVn5+vmudcePG6Z133tGbb76pL774QidPntSwYcNUWlpa/08CoN6oa8B6i785pLGvb9QhZ4Hb8hxngca+vrHOTQF1DaDWzEWQZN555x3X87KyMhMfH2/+9Kc/uZYVFBQYh8Nh5s2bZ4wx5sSJEyY0NNS8+eabrnUOHDhggoKCzOLFi2v1d51Op5FknE7nxYQPBIy61IxVdV3XOIHGpKS0zFw99RPT9v8+rPKR/H8fmqunfmJKSsvctqttzVDXgPUOHD9tpn68zRzOL7jgelbUjEcvKs7KylJOTo6GDBniWma32zVgwACtWrVKkrRhwwYVFxe7rZOYmKiuXbu61jlfYWGh8vLy3B4AGoa36lqitoEKa7OOVRoZOJeRdMhZoLVZxzzy96hroOHNX7FL81fs1m/+vtnqUCrx6EXFOTk5kqS4uDi35XFxcdq7d69rnbCwMDVr1qzSOhXbn2/atGmaPHmyJ0NFAEue8JHVIXjcnj8N9dp7e6uuJWobqJCbX30zUJ/1akJdAw0rN69Ab6zLliSNvradxdFU5pVpR8+/s5oxpsa7rV1onYkTJ8rpdLoe2dnZHosVQO14uq4lahuo0Co63KPr1RZ1DTSMBZ/vVlFJmXq1baa+7WOtDqcSjzYE8fHxklTpyEFubq7rKER8fLyKiop0/Pjxatc5n91ud93ynFufAw3LW3UtUdtAhatSmivBEa7qdrNtkhIc5VOQegJ1DTScoycL9bf/7pMkPTSwQ41NtxU82hCkpKQoPj5eS5cudS0rKirSihUrlJaWJknq1auXQkND3dY5dOiQvvnmG9c6AHwHdQ14X3CQTZNu6ixJlZqCiueTburssfsRUNdAw3npiyydKS5V9ySHBnRsaXU4VarzNQQnT57U999/73qelZWlTZs2qXnz5mrTpo3GjRunqVOnKjU1VampqZo6daoiIyM1fPhwSZLD4dB9992n3/zmN4qNjVXz5s3129/+Vt26ddP111/vuU8GoNaoa8B6N3RN0Nx7rtDkD7a5XWAc7wjXpJs664auCXV6P+oasN6J00V6bdUeSdJDA1N9cnRAqkdDsH79el133XWu5+PHj5ckjRgxQq+++qoee+wxnTlzRhkZGTp+/Lj69OmjJUuWKDo62rXNs88+q5CQEN111106c+aMBg0apFdffVXBwcEe+EgA6oq6BnzDDV0TNLhzvEfuVExdA9Z7+cs9OlVUqk4JMbq+Uyurw6mWzRhjrA6irvLy8uRwOOR0Ojk3EXUWiLMM+UvN+EucgK/wh5rxhxgBb8grKFa/Py1TfkGJ5tx9hX7UrXajfFbUjFdmGQIAAAAC2cIv9yi/oESprZrohi7xVodzQTQEAAAAgAedLCzRX77MkiQ9OLCDgjw0IYC30BAAAAAAHvT6mr06cbpYKS2iNKx7otXh1IiGAAAAAPCQM0WlemnlbklSRnp7j00X7E00BAAAAICHLFq7T0dOFimpWYRu7XmJ1eHUCg0BAAAA4AEFxaWav2KXJCkjvYNCg/1jV9s/ogQAAAB83D/WZys3v1AJjnDd0cs/RgckGgIAAADgohWVlGnu8vLRgTED2sse4j838KMhAAAAAC7Svzbu10FngVpG2/WTK1tbHU6d0BAAAAAAF6GktExzzo4OjL62ncJD/Wd0QKIhAAAAAC7Ke5sOat+x02oeFabhfdpYHU6d0RAAAAAA9VRaZvTCZ99Lkkb1T1FkWIjFEdUdDQEAAABQTx99fUi7j5ySIyJUv+ibbHU49UJDAAAAANRDWZnR7GU7JUn39ktRE7v/jQ5INAQAAABAvSzZlqPvfjipaHuIRvZLtjqceqMhAAAAAOrIGKPnl5VfOzAiLVmOiFCLI6o/GgIAAACgjpZ9m6utB/MUGRase69JsTqci0JDAAAAANSBMUbPnR0d+PnVbdU8KsziiC4ODQEAAABQByt3HtHm7BMKDw3SqP7trA7notEQAAAAALVUfu1A+cxCw69qq5bRdosjung0BAAAAEAtrdl9TOv2HFdYSJBGD/D/0QGJhgAAAACotYrRgZ/0bq24mHCLo/EMGgIAAACgFjbsPaZVu44qNNimMentrQ7HY2gIAAAAgFp47tPymYXuuCJJlzSNsDgaz6EhAAAAAGqwKfuEVnx3WMFBNmWkd7A6HI+iIQAAAABqMPvstQO3XJ6oNrGRFkfjWTQEAAAAwAVsPejUJ9tzZbNJD1zXuEYHJBoCAAAA4IJmn70r8bDuiWrfsonF0XgeDQEAAABQje9+yNe/v8mRJD3YCEcHJBoCAAAAoFoVowM3dInXpfHRFkfjHTQEAAAAQBV2Hz6pD7cclCQ9OLBxjg5INAQAaqGkpES/+93vlJKSooiICLVr105TpkxRWVmZax1jjDIzM5WYmKiIiAilp6dr69atFkYN4EKoa6BmL3y2S2VGGnRZK3W9xGF1OF5DQwCgRtOnT9e8efM0e/Zsbd++XTNmzNBTTz2l559/3rXOjBkzNHPmTM2ePVvr1q1TfHy8Bg8erPz8fAsjB1Ad6hq4sH1HT+vdTQckSQ8NSrU4Gu/yeEPAEQeg8Vm9erVuueUWDR06VMnJybrzzjs1ZMgQrV+/XlJ5Tc+aNUuPP/64br/9dnXt2lULFy7U6dOntWjRIoujB1AV6hq4sLkrvldpmVH/1Ba6vHVTq8PxKo83BBxxABqfa665Rp9++qm+++47SdLmzZv1xRdf6Ec/+pEkKSsrSzk5ORoyZIhrG7vdrgEDBmjVqlXVvm9hYaHy8vLcHgAaBnUNVO/AiTP654b9kqSHG/nogCSFePoNzz3iIEnJycl64403qj3iIEkLFy5UXFycFi1apNGjR3s6JAAX6f/+7//kdDp12WWXKTg4WKWlpXryySf1s5/9TJKUk1M+HVtcXJzbdnFxcdq7d2+17ztt2jRNnjzZe4EDqBZ1DVRv/opdKi41urpdc12Z3NzqcLzO4yME3jjiwNEGwFpvvfWWXn/9dS1atEgbN27UwoUL9fTTT2vhwoVu69lsNrfnxphKy841ceJEOZ1O1yM7O9sr8QOojLoGqpabV6A315X/u314YOMfHZC8MELgjSMOHG0ArPXoo49qwoQJ+ulPfypJ6tatm/bu3atp06ZpxIgRio+Pl1Re3wkJCa7tcnNzK9X6uex2u+x2u3eDB1Al6hqo2vzPd6uopEy92jZT3/axVofTIDw+QuCNIw4cbQCsdfr0aQUFuf9cBAcHuyYLSElJUXx8vJYuXep6vaioSCtWrFBaWlqDxgqgdqhroLIjJwv1t/+WH6B+aGCHC46GNSYeHyHwxhEHjjYA1rrpppv05JNPqk2bNurSpYu++uorzZw5U/fee6+k8gZ/3Lhxmjp1qlJTU5WamqqpU6cqMjJSw4cPtzh6AFWhroHKXlqZpYLiMnVPcmhAx5ZWh9NgPN4Q1OWIQ8+ePSX974jD9OnTPR0OAA94/vnn9fvf/14ZGRnKzc1VYmKiRo8erT/84Q+udR577DGdOXNGGRkZOn78uPr06aMlS5YoOrpx3uYd8HfUNeDu+Kki/XX1HknSQwNTA2Z0QJJsxhjjyTccOXKkPvnkE82fP991xOH+++/Xvffe69rhnz59uqZNm6ZXXnnFdcRh+fLl2rFjR61+ZPLy8uRwOOR0OhUTE+PJ8BEAkid8ZHUIHrfnT0Mv+Lq/1Iy/xAn4Cn+oGX+IEZCkmUt26Lll36tTQow+fvgayxoCK2rG4yMEHHEAAACAP8krKNYrq/ZICqxrByp4vCGIjo7WrFmzNGvWrGrXsdlsyszMVGZmpqf/PAAAAFAnC7/co/yCEqW2aqIbusRbHU6D8/gsQwAAAIC/OFlYor98mSVJenBgBwUFBdbogERDAAAAgAD2+pq9OnG6WO1aRGlY90Srw7EEDQEAAAAC0pmiUr20crckKeO6DgoOwNEBiYYAAAAAAWrR2n06crJIrZtH6JbLA3N0QKIhAAAAQAAqKC7V/BW7JEkZ6R0UGhy4u8WB+8kBAAAQsP6+Plu5+YVKdITrjiuSrA7HUjQEAAAACChFJWWat7x8dGBMenuFhQT2LnFgf3oAAAAEnLc37tdBZ4FaRdt1V+/WVodjORoCAAAABIzi0jLNWf69JOn+a9spPDTY4oisR0MAAACAgPHepoPKPnZGsVFhurtPW6vD8Qk0BAAAAAgIpWVGcz4rHx0Y1b+dIsIYHZBoCAAAABAgPtxyULuPnFLTyFD9vC+jAxVoCAAAANDolZUZvXB2dODefilqYg+xOCLfQUMAAACARu8/W3P03Q8nFW0P0Yi0ZKvD8Sk0BAAAAGjUjDF6fln56MDIfslyRIRaHJFvoSEAAABAo/bp9lxtO5SnqLBg3dsvxepwfA4NAQAAABqt8tGBnZKke/q2VbOoMIsj8j00BAAAAGi0Pt95RJv3OxUeGqRf9W9ndTg+iYYAAAAAjZIxRs9/Wj46MPyqtmrRxG5xRL6JhgAAAACN0urdR7V+73GFhQRp9ABGB6pDQwAAAIBG6flPy2cW+knv1oqLCbc4Gt9FQwAAAIBGZ/2eY1q9+6hCg20ak97e6nB8Gg0BAAAAGp3nzt534I4rknRJ0wiLo/FtNAQAAABoVDZln9Dn3x1WcJBNGekdrA7H59EQAAAAoFGZffa+A7dcnqg2sZEWR+P7aAgAAADQaGw96NQn23MVZJMeuI7RgdqgIQAAAECjMfvstQPDuieqfcsmFkfjH2gIAAAA0Ch890O+/v1NjiTpwYGMDtQWDQEAAAAahYrRgRu7xqtjXLTF0fgPGgIAAAD4vV2HT+rDLQclMTpQVzQEAGrlwIEDuueeexQbG6vIyEhdfvnl2rBhg+t1Y4wyMzOVmJioiIgIpaena+vWrRZGDKAm1DUakxc++15lRrq+Uyt1SXRYHY5foSEAUKPjx4+rX79+Cg0N1b///W9t27ZNzzzzjJo2bepaZ8aMGZo5c6Zmz56tdevWKT4+XoMHD1Z+fr51gQOoFnWNxmTf0dN6b1P56MBDA1Mtjsb/hFgdAADfN336dLVu3VqvvPKKa1lycrLr/40xmjVrlh5//HHdfvvtkqSFCxcqLi5OixYt0ujRo6t838LCQhUWFrqe5+XleecDAKiEukZjMmf59yotM7q2Y0v1aN3U6nD8jldGCBiCBBqX999/X71799aPf/xjtWrVSj179tSLL77oej0rK0s5OTkaMmSIa5ndbteAAQO0atWqat932rRpcjgcrkfr1q29+jkA/A91jcbiwIkzenvjfknSw1w7UC8ebwgYggQan927d2vu3LlKTU3Vf/7zH40ZM0YPP/ywXnvtNUlSTk75FG9xcXFu28XFxbleq8rEiRPldDpdj+zsbO99CABuqGs0FvOW71JxqVHfdrHqndzc6nD8ksdPGfLWECQA65SVlal3796aOnWqJKlnz57aunWr5s6dq1/84heu9Ww2m9t2xphKy85lt9tlt9u9EzSAC6Ku0Rj8kFegt9aXN50PDWJ0oL48PkLgjSHIwsJC5eXluT0ANJyEhAR17tzZbVmnTp20b98+SVJ8fLwkVTpqmJubW+noIgDfQF2jMZi/YreKSsrUu20z9W0Xa3U4fsvjDYE3hiA5HxGwVr9+/bRjxw63Zd99953atm0rSUpJSVF8fLyWLl3qer2oqEgrVqxQWlpag8YKoHaoa/i7IycLtWjtXknSQ4NSLzhyhQvz+ClD3hiCnDhxosaPH+96npeXR1MANKBHHnlEaWlpmjp1qu666y6tXbtWCxYs0IIFCySV1/O4ceM0depUpaamKjU1VVOnTlVkZKSGDx9ucfQAqkJdw9+9uHK3CorL1CPJoWtTW1gdjl/zeENQ3RDk22+/Lcl9CDIhIcG1zoWGIDkfEbDWlVdeqXfeeUcTJ07UlClTlJKSolmzZunuu+92rfPYY4/pzJkzysjI0PHjx9WnTx8tWbJE0dHcOh7wRdQ1/NnxU0X66+qzowMDGR24WB5vCOoyBNmzZ09J/xuCnD59uqfDAeAhw4YN07Bhw6p93WazKTMzU5mZmQ0XFICLQl3DX738ZZZOF5Wqc0KMBnVqZXU4fs/jDQFDkAAAAPAW55livfrlHknSQwM7MDrgAR5vCBiCBAAAgLcsXLVH+YUl6hjXRP9fl3irw2kUPN4QSAxBAgAAwPNOFpbo5S+zJEkPXNdBQUGMDniCx6cdBQAAALzhr6v36sTpYrVrEaVh3ROtDqfRoCEAAACAzztdVKKXVu6WJGVc10HBjA54DA0BAAAAfN6i/+7T0VNFat08QrdczuiAJ9EQAAAAwKcVFJdqwednRwfSOyg0mF1YTyKbAAAA8Gl/X5+t3PxCJTrCdccVSVaH0+jQEAAAAMBnFZWUad7yXZKkMentFRbC7qunkVEAAAD4rLc37tdBZ4FaRdt1V+/WVofTKNEQAAAAwCcVl5ZpzvLvJUmjB7RXeGiwxRE1TjQEAAAA8EnvbTqo7GNn1KJJmIZf1cbqcBotGgIAAAD4nNIyozmflY8OjOrfThFhjA54Cw0BAAAAfM6HWw5q95FTahoZqnuubmt1OI0aDQEAAAB8SlmZ0exl5aMD9/VLURN7iMURNW40BAAAAPApi7fmaGfuSUWHh2hEv2Srw2n0aAgAAADgM4wxev7s6MAv05IVEx5qcUSNHw0BAAAAfMYn23O1/VCeosKCde81KVaHExBoCAAAAOATykcHdkqSft43WU0jwyyOKDDQEAAAAMAnrPjusLbsdyo8NEij+jM60FBoCAAAAGC5c68duLtPW7VoYrc4osBBQwAAAADLrd51VBv2HldYSJBGX9vO6nACCg0BAAAALPfc2WsHfnpla7WKCbc4msBCQwAAAABLrdtzTGt2H1NosE1jBrS3OpyAQ0MAAAAASz33afnowJ29kpTYNMLiaAIPDQEAAAAssyn7hFbuPKLgIJvGDuhgdTgBiYYAAAAAlnn+7OjArZdfojaxkRZHE5hoCAAAAGCJbw449em3uQqySQ9cx7UDVqEhAAAAgCVmn73vwLDuiWrXsonF0QQuGgIAAAA0uB05+Vq8NUeS9OBArh2wEg0BAAAAGtzsz8pHB27sGq+OcdEWRxPYaAgA1Nm0adNks9k0btw41zJjjDIzM5WYmKiIiAilp6dr69at1gUJoE6oazSkXYdP6sMtByUxOuALaAgA1Mm6deu0YMECde/e3W35jBkzNHPmTM2ePVvr1q1TfHy8Bg8erPz8fIsiBVBb1DUa2guffS9jpOs7tVKXRIfV4QQ8GgIAtXby5EndfffdevHFF9WsWTPXcmOMZs2apccff1y33367unbtqoULF+r06dNatGiRhREDqAl1jYa27+hpvbepfHTgoYGpFkcDiYYAQB088MADGjp0qK6//nq35VlZWcrJydGQIUNcy+x2uwYMGKBVq1ZV+36FhYXKy8tzewBoWNQ1Gtqc5d+rtMzo2o4t1aN1U6vDgaQQqwMA4B/efPNNbdy4UevWrav0Wk5O+SwRcXFxbsvj4uK0d+/eat9z2rRpmjx5smcDBVBr1DUa2oETZ/T2xv2SpF8P4toBX+H1EQIuUgL8X3Z2tn7961/r9ddfV3h4eLXr2Ww2t+fGmErLzjVx4kQ5nU7XIzs722MxA7gw6hpWmLd8l4pLjdLax6pX2+ZWh4OzvNoQcJES0Dhs2LBBubm56tWrl0JCQhQSEqIVK1boueeeU0hIiOsIYsURxQq5ubmVji6ey263KyYmxu0BoGFQ12hoOc4CvbWuvEHk2gHf4rWGgIuUgMZj0KBB+vrrr7Vp0ybXo3fv3rr77ru1adMmtWvXTvHx8Vq6dKlrm6KiIq1YsUJpaWkWRg6gOtQ1Gtr8z3epqLRMVyY309XtGB3wJV67huDci5SeeOIJ1/KaLlIaPXp0pfcqLCxUYWGh6zkXKAENKzo6Wl27dnVbFhUVpdjYWNfycePGaerUqUpNTVVqaqqmTp2qyMhIDR8+3IqQAdSAukZDOpxfqEX/3SepfHTgQqedoeF5pSHw9EVKXKAE+L7HHntMZ86cUUZGho4fP64+ffpoyZIlio7m7pOAv6Ku4SkvrdytwpIy9WjdVP1TW1gdDs7j8Yag4iKlJUuWeOwipYkTJ2r8+PGu53l5eWrdurVnAgZQL8uXL3d7brPZlJmZqczMTEviAXDxqGt4w7FTRfrrmvKDvg8P7MDogA/yeENw7kVKFUpLS/X5559r9uzZ2rFjh6TykYKEhATXOhe6SMlut8tut3s6VAAAAHjZy19k6XRRqbokxmjgZa2sDgdV8PhFxVykBAAAAElyninWwlV7JEkPMTrgszw+QsBFSgAAAJCkV7/co/zCEl0aF60hneOtDgfVsOROxVykBAAA0LjlFxTr5S+zJEkPDOygoCBGB3xVgzQEXKQEAAAQWP66Zq+cZ4rVrmWUhnZLqHkDWMardyoGAABA4DldVKKXVp4dHUjvoGBGB3waDQEAAAA8atF/9+nYqSK1aR6pWy5PtDoc1ICGAAAAAB5TUFyq+Z/vliRlpLdXSDC7m76ObwgAAAAe89a6bB3OL9QlTSN0+xVJVoeDWqAhAAAAgEcUlpRq3opdkqQxA9opLIRdTX/AtwQAAACPeHvDAR1yFqhVtF0/7t3a6nBQSzQEAAAAuGjFpWWas/x7SdLoAe0VHhpscUSoLRoCAAAAXLR3vzqg/cfPqEWTMA2/qo3V4aAOaAgAAABwUUrLjOYsL792YFT/dooIY3TAn9AQAAAA4KJ8uOWgso6cUtPIUN1zdVurw0Ed0RAAAACg3srKjGYvK7924L5+KWpiD7E4ItQVDQEAAADqbfHWHO3MPano8BCN6JdsdTioBxoCAAAA1IsxRs+fHR34ZVqyYsJDLY4I9UFDAAAAgHr5ZHuuth/KU1RYsO69JsXqcFBPNAQAAACos/LRgZ2SpF+kJatpZJjFEaG+aAgAAABQZyu+O6wt+52KCA3WKEYH/BoNAQAAAOrEGKPnPi0fHbi7TxvFNrFbHBEuBg0BAAAA6mTVrqPauO+EwkKCdP+17awOBxeJhgAAAAB1UjE68LMrW6tVTLjF0eBi0RAAAACg1tZmHdN/s44pNNim0QPaWx0OPICGAAAAALVWMbPQnb1aK7FphMXRwBNoCAAAAFArX+07rpU7jyg4yKaMdEYHGgsaAgAAANRKxV2Jb+t5iVo3j7Q4GngKDQEAAABq9M0Bp5Z9m6sgm/TAdR2sDgceREMAAACAGlVcO3BTj0SltIiyOBp4Eg0BAAAALujbnDz9Z+sPstmkBxkdaHRoCAAAAHBBs89eO3Bj13ilxkVbHA08jYYAQI2mTZumK6+8UtHR0WrVqpVuvfVW7dixw20dY4wyMzOVmJioiIgIpaena+vWrRZFDKAm1DVq6/vck/ro60OSpAevS7U4GngDDQGAGq1YsUIPPPCA1qxZo6VLl6qkpERDhgzRqVOnXOvMmDFDM2fO1OzZs7Vu3TrFx8dr8ODBys/PtzByANWhrlFbcz77XsZI13eKU+fEGKvDgReEWB0AAN+3ePFit+evvPKKWrVqpQ0bNujaa6+VMUazZs3S448/rttvv12StHDhQsXFxWnRokUaPXp0le9bWFiowsJC1/O8vDzvfQgAbqhr1Mbeo6f03uaDkqSHB3HtQGPFCAGAOnM6nZKk5s2bS5KysrKUk5OjIUOGuNax2+0aMGCAVq1aVe37TJs2TQ6Hw/Vo3bq1dwMHUC3qGlWZ89kulZYZDejYUt2TmlodDryEhgBAnRhjNH78eF1zzTXq2rWrJCknJ0eSFBcX57ZuXFyc67WqTJw4UU6n0/XIzs72XuAAqkVdoyr7j5/W2xv3S2J0oLHjlCEAdfLggw9qy5Yt+uKLLyq9ZrPZ3J4bYyotO5fdbpfdbvd4jADqhrpGVeat2KWSMqO09rHq1ba51eHAizw+QsCsBUDj9dBDD+n999/XZ599pqSkJNfy+Ph4Sap01DA3N7fS0UUAvoW6RlVynAX6+7ry0YGHBjKzUGPn8YaAWQuAxscYowcffFD/+te/tGzZMqWkpLi9npKSovj4eC1dutS1rKioSCtWrFBaWlpDhwugFqhrXMj8z3epqLRMVyY309XtGB1o7Dx+ypC3Zi0AYJ0HHnhAixYt0nvvvafo6GjXEUOHw6GIiAjZbDaNGzdOU6dOVWpqqlJTUzV16lRFRkZq+PDhFkcPoCrUNapzOL9Qi/67T1L56MCFThFD4+D1awjqOmtBVQ0BU5gB1po7d64kKT093W35K6+8opEjR0qSHnvsMZ05c0YZGRk6fvy4+vTpoyVLlig6mjtaAr6IukZ1Xlq5W4UlZerRuqn6p7awOhw0AK82BHWdtWDv3r1Vvs+0adM0efJkb4YK4AKMMTWuY7PZlJmZqczMTO8HBOCiUdeoyrFTRfrrmvL9sYcHdmB0IEB4ddrRilkL3njjjUqv1WXWAqYwAwAA8L6Xv8jS6aJSdUmM0cDLWlkdDhqI10YIKmYt+Pzzz6udtSAhIcG1/EKzFjCFGQAAgHc5zxRr4ao9krh2INB4fISAWQsAAAD8z6tf7lF+YYkujYvWkM5MLRtIPD5CwKwFAAAA/iW/oFh/+WK3JOnBgR0UFMToQCDxeEPArAUAAAD+5bXVe5VXUKJ2LaP0o24JNW+ARsXjDQGzFgAAAPiP00Ul+ssXWZKkB6/roGBGBwKOV2cZAgAAgG/725p9OnaqSG1jI3Vzj0Srw4EFaAgAAAACVEFxqeZ/Xn7tQEZ6e4UEs2sYiPjWAQAAAtSba/fpyMlCXdI0Qrf1TKp5AzRKXr1TMQDAv5WWGa3NOqbc/AK1ig7XVSnNOb8YaCQKS0o1b0X56MCY9PYKC+E4caCiIQAAVGnxN4c0+YNtOuQscC1LcIRr0k2ddUNXZiEB/N0/N+xXTl6B4mLs+nEvRgcCGa0gAKCSxd8c0tjXN7o1A5KU4yzQ2Nc3avE3hyyKDIAnFJeWae7yXZKk0de2V3hosMURwUo0BAAAN6VlRpM/2KaqJpGuWDb5g20qLat5mmkAvqWszOj73JOa9cl32n/8jFo0CdPPrmpjdViwGKcMAQDcrM06Vmlk4FxG0iFngdZmHVPf9rENFxiAOjHGKPvYGW3ef0JfH3Bqy/4T+uZAnk4WlrjW+VX/dooIY3Qg0NEQAADc5OZX3wzUZz0A3meMUU5egTZnO/X1gRPast+prw84deJ0caV1w0OD1DXRoWs7ttS916RYEC18DQ0BAMBNq+hwj64HwPOOnCzU1/ud5Uf/9zu1eb9TR04WVlovLDhInRKi1S3Joe5JTdU9yaEOLZtwvwG4oSEAALi5KqW5EhzhynEWVHkdgU1SvKN8ClIA3uc8XayvD/xv53/L/hM6WMVpfcFBNnWMi1b3SxzqluRQj6Sm6hjfRPYQTgnChdEQAADcBAfZNOmmzhr7+kbZJLemoOIOBJNu6sz9CAAvOFlYoq0HnNqy36ktZ8/733v0dKX1bDapfcsmrp3/7klN1TkhhusBUC80BACASm7omqC591xR6T4E8dyHAPCYguJSbTuUpy3ZJ87u/Du16/BJmSqG5trGRqrbJQ51P7vz3yUxRtHhoQ0fNBolGgIAQJVu6JqgwZ3juVMx4AFFJWX67od8t3P+v/shv8rpexMc4a4d/+5JDnW7xKGmkWEWRI1AQUMAAKhWcJCNqUWBOio9O9e/65z/A05tP5SnopKySuu2aBKm7klN1e0Sh3q0dqjrJQ4u2EeDoyEIEMkTPrI6BK/Y86ehVocAAAhgZWVGe46eKj/nf3/5lJ/fHMjTmeLSSus6IkJdR/wrjv4nOMJlszHqBmvREAAAANSCMUb7j585e8Fv+dH/rw84lV9QUmndqLBgdT3nnP/uSQ61aR7Jzj98Eg0BAABAFX7IK9Dm7Iq7/Jbv/B87VVRpPXtIkLokxrid+pPSognX28Bv0BAAAICAd/RkobYccJ6d5798us/c/Mo3+goJsumyhOjyo/5nT/1JjWuiUG70BT9GQwAAAAKK80yxth4on+nn6wMntDnbqQMnzlRaL8gmdYyLdpvu89L4aIWHMtc/GhcaAgAA0GidLirR1oN5bqf+ZB05VeW67VpElV/0m9RUPZIc6pwYo8gwdpXQ+PGvHAAANAoFxaX6NidfW/afcJ32833uSVUx1b+SmkWoR1LTs3f5LZ/uM4YbfSFA0RAA8BulZYabZAGQJBWXlt/oa8s55/zvyMlXSRV7/3Exdtc5/93OnvrTPIobfQEVaAgA+IXF3xzS5A+26ZCzwLUswRGuSTd11g1dEyyMDIC3lZYZ7T58svyc//0ntHm/U9uqudFX86iw8vP9Lyk/9ad7kkNxMdzoC7gQGgIAPm/xN4c09vWNOv+4X46zQGNf36i591xBUwA0EsYY7T162u0uv1sPOHWqqPKNvqLDQ9xu8tU9yaFLmkYw1z9QRzQEADxqzpw5euqpp3To0CF16dJFs2bNUv/+/ev9fqVlRpM/2FapGZAkI8kmafIH2zS4czynDwFe4um6PteZolIt35F7zpSfJ5RXxY2+IkKD1fWSmHN2/puqbfNIBVH3wEWjIQDgMW+99ZbGjRunOXPmqF+/fpo/f75uvPFGbdu2TW3atKnXe67NOuZ2mtD5jKRDzgKtzTqmvu1j6xk5gOp4o67PVVhSqrF/2+i2LCw4SJ0SY9QjyXH2Rl9N1b4lN/oCvIWGAIDHzJw5U/fdd59GjRolSZo1a5b+85//aO7cuZo2bVq93jM3v/pmoD7rAagbb9T1uZpGhun6Tq3UMtqubpeUH/3vGBetsBBu9AU0FBoCAB5RVFSkDRs2aMKECW7LhwwZolWrVlW5TWFhoQoL/3cn0Ly8vErrtIqu3cWAtV0PQO15q67P99KIKy8uUAAXhfYbgEccOXJEpaWliouLc1seFxennJycKreZNm2aHA6H69G6detK61yV0lwJjnBVd6KATeWzDV2V0vwiPwGA83mrrgH4FhoCAB51/uwexphqZ/yYOHGinE6n65GdnV1pneAgmybd1Ln8vc//W2f/O+mmzpxbDHiRp+sagG/hlCEAHtGiRQsFBwdXOmqYm5tb6ehiBbvdLrvdXuN739A1QXPvuaLSfQjiuQ8B4FXerGsAvoOGAIBHhIWFqVevXlq6dKluu+021/KlS5fqlltuuej3v6FrggZ3judOxUAD8nZdA/ANlp4yNGfOHKWkpCg8PFy9evXSypUrrQwHwEUaP368XnrpJb388svavn27HnnkEe3bt09jxozxyPsHB9nUt32sbrn8EvVtH0szADQAb9c1AOtZNkLg7XmNATS8n/zkJzp69KimTJmiQ4cOqWvXrvr444/Vtm1bq0MDUE/UNdD42YwxVd0A1Ov69OmjK664QnPnznUt69Spk2699dYa5zXOy8uTw+GQ0+lUTEzMBddNnvCRR+L1JXv+NLTO2zTGPEjkokJNeahLzVjJX+IEfIU/1Iw/xAj4EitqxpIRgrrOa3z+nMZOp1NS7eY2Lis8fZHR+p7afO7zNcY8SOSiQk15qHjdov6/1iriq8/3CgQif6ht6hqoGyvq2pKGoK7zGk+bNk2TJ0+utDxQ5zZ2zLI6At9BLsrVNg/5+flyOBxejeVi5OfnSwrc2gbqy5drm7oG6qch69rSWYZqO6/xxIkTNX78eNfzsrIyHTt2TLGxsdXOg9yQ8vLy1Lp1a2VnZwf8cCi5+B9fyoUxRvn5+UpMTLQ0jpokJiYqOztb0dHR1da2L+U1UJDzhlfbnPtDbVPXvomcW6M2ebeiri1pCOo6r3FVcxo3bdrUmyHWS0xMDEV1Frn4H1/Jha8ePTxXUFCQkpKSarWur+Q1kJDzhlebnPt6bVPXvo2cW6OmvDd0XVsy7ei58xqfa+nSpUpLS7MiJAAAACAgWXbK0Pjx4/Xzn/9cvXv3Vt++fbVgwQLmNQYAAAAamGUNQWOa19hut2vSpEncql3k4lzkwjvIa8Mj5w0v0HIeaJ/XF5Bza/hq3i27DwEAAAAA61lyDQEAAAAA30BDAAAAAAQwGgIAAAAggNEQAAAAAAGMhgA+bfny5bLZbDpx4oTVoQDwIGobaHyoa/9FQwAAAAAEMBqC8xQVFVkdgk8gD+7Ih3eQ14ZHzq0RSHkPpM/qK8i5NRpT3ht9Q5Cfn6+7775bUVFRSkhI0LPPPqv09HSNGzdOkpScnKwnnnhCI0eOlMPh0K9+9StJ0ttvv60uXbrIbrcrOTlZzzzzjNv72mw2vfvuu27LmjZtqldffVWStGfPHtlsNr355ptKS0tTeHi4unTpouXLl9cq7tLSUt13331KSUlRRESELr30Uv35z38OuDxU+PLLL9WjRw+Fh4erT58++vrrr+uTBhd/zsfWrVs1dOhQxcTEKDo6Wv3799euXbvqmwqP8te8erreGpK/5ryCp2u7ofhz3uv6G+Kvn5W6pq7ryp/zftH7BqaRGzVqlGnbtq355JNPzNdff21uu+02Ex0dbX79618bY4xp27atiYmJMU899ZTZuXOn2blzp1m/fr0JCgoyU6ZMMTt27DCvvPKKiYiIMK+88orrfSWZd955x+1vORwO1zpZWVlGkklKSjL//Oc/zbZt28yoUaNMdHS0OXLkSI1xFxUVmT/84Q9m7dq1Zvfu3eb11183kZGR5q233gqoPHz22WdGkunUqZNZsmSJ2bJlixk2bJhJTk42RUVF9cqFP+dj//79pnnz5ub2228369atMzt27DAvv/yy+fbbb+udC0/y17x6ut4akr/m3Fu13VD8Ne/1+Q3x189KXVPXdeWveffEvkGjbgjy8vJMaGio+cc//uFaduLECRMZGen25d56661u2w0fPtwMHjzYbdmjjz5qOnfu7Hpe2y/3T3/6k+v14uJik5SUZKZPn16vz5ORkWHuuOOOOm/nz3mo+HF58803XcuOHj1qIiIi6v2j7s/5mDhxoklJSfHJH1Z/zmtV6ltvDcmfc+6N2m4o/pz3uv6G+PNnrQp1TV1Xx5/z7ol9g0Z9ytDu3btVXFysq666yrXM4XDo0ksvdVuvd+/ebs+3b9+ufv36uS3r16+fdu7cqdLS0jrF0LdvX9f/h4SEqHfv3tq+fXuttp03b5569+6tli1bqkmTJnrxxRe1b9++Ov19yf/zcP72zZs316WXXlqn7c/lz/nYtGmT+vfvr9DQ0Dr9vYbgz3mVPFdvDcnfc37+9hdb2w3Fn/Ne198Qf/6sEnVNXdeeP+fdE/sGjbohKG/Kys/dqmp5haioqEqv17SNzWartKy4uLhWcZ3/3lX5+9//rkceeUT33nuvlixZok2bNumXv/xlvS5g8ec8eGN7f85HRERErd7LCv6cV0/WW0Py55x7c3tv8+e81/U3xJ8/K3VNXdeFP+fdE/sGjbohaN++vUJDQ7V27VrXsry8PO3cufOC23Xu3FlffPGF27JVq1apY8eOCg4OliS1bNlShw4dcr2+c+dOnT59utJ7rVmzxvX/JSUl2rBhgy677LIaY1+5cqXS0tKUkZGhnj17qkOHDvW+cNSf81DV9sePH9d3331Xp+3P5c/56N69u1auXFnrH5KG5M959WS9NSR/znlV219sbTcUf857XX9D/PmzUtfUdV34c949sm9Q75ON/MSoUaNMSkqKWbZsmfnmm2/MHXfcYaKjo824ceOMMeXngz377LNu22zYsMHtApFXX3210gUiP/3pT02nTp3Mhg0bzLp168zAgQNNaGhopfPB2rRpY/71r3+Z7du3m/vvv980adLEHD58uMa4Z82aZWJiYszixYvNjh07zO9+9zsTExNjevToEVB5qDgfsUuXLq6LfG6++WbTpk0bU1hYWK9c+HM+jhw5YmJjY10XDn333Xfmtdde86mLiv0xr56ut4bkrzn3Vm03FH/Ne31+Q/z1s1LX1HVd+WvePbFv0Ogbgry8PDN8+HATGRlp4uPjzcyZM81VV11lJkyYYIyp+ss1xph//vOfpnPnziY0NNS0adPGPPXUU26vHzhwwAwZMsRERUWZ1NRU8/HHH1d5gciiRYtMnz59TFhYmOnUqZP59NNPaxV3QUGBGTlypHE4HKZp06Zm7NixZsKECfX+IfPXPFT8uHzwwQemS5cuJiwszFx55ZVm06ZN9cqDv+fDGGM2b95shgwZYiIjI010dLTp37+/2bVrV71z4Un+mldP11tD8tece6u2G4q/5t2Yuv+G+Otnpa6p67ry17wbc/H7Bo2+ITjfyZMnjcPhMC+99JJX/07Fl/vVV1959e/UF3lwRz68g7w2PHJujUDKeyB9Vl9Bzq0RSHkPqf/JRv7hq6++0rfffqurrrpKTqdTU6ZMkSTdcsstFkfWsMiDO/LhHeS14ZFzawRS3gPps/oKcm6NQM57o76ouMLTTz+tHj166Prrr9epU6e0cuVKtWjRwtKYxowZoyZNmlT5GDNmjFf+JnlwRz68g7w2PHJujUDKeyB9Vl9Bzq0RqHm3GXPePEhoELm5ucrLy6vytZiYGLVq1aqBI7IGeXBHPryDvDY8cm6NQMp7IH1WX0HOrdEQeachAAAAAAJYQJwyBAAAAKBqNAQAAABAAKMhAAAAAAIYDQEAAAAQwGgIAAAAgABGQwAAAAAEMBoCAAAAIID9/5XIxW1/Xr0fAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n## Controlling line properties\n\nLines have many attributes that you can set: linewidth, dash style,\nantialiased, etc; see `matplotlib.lines.Line2D`.  There are\nseveral ways to set line properties\n\n* Use keyword arguments::\n\n      plt.plot(x, y, linewidth=2.0)\n\n\n* Use the setter methods of a ``Line2D`` instance.  ``plot`` returns a list\n  of ``Line2D`` objects; e.g., ``line1, line2 = plot(x1, y1, x2, y2)``.  In the code\n  below we will suppose that we have only\n  one line so that the list returned is of length 1.  We use tuple unpacking with\n  ``line,`` to get the first element of that list::\n\n      line, = plt.plot(x, y, '-')\n      line.set_antialiased(False) # turn off antialiasing\n\n* Use `~.pyplot.setp`.  The example below\n  uses a MATLAB-style function to set multiple properties\n  on a list of lines.  ``setp`` works transparently with a list of objects\n  or a single object.  You can either use python keyword arguments or\n  MATLAB-style string/value pairs::\n\n      lines = plt.plot(x1, y1, x2, y2)\n      # use keyword arguments\n      plt.setp(lines, color='r', linewidth=2.0)\n      # or MATLAB style string value pairs\n      plt.setp(lines, 'color', 'r', 'linewidth', 2.0)\n\n\nHere are the available `~.lines.Line2D` properties.\n\n======================  ==================================================\nProperty                Value Type\n======================  ==================================================\nalpha                   float\nanimated                [True | False]\nantialiased or aa       [True | False]\nclip_box                a matplotlib.transform.Bbox instance\nclip_on                 [True | False]\nclip_path               a Path instance and a Transform instance, a Patch\ncolor or c              any matplotlib color\ncontains                the hit testing function\ndash_capstyle           [``'butt'`` | ``'round'`` | ``'projecting'``]\ndash_joinstyle          [``'miter'`` | ``'round'`` | ``'bevel'``]\ndashes                  sequence of on/off ink in points\ndata                    (np.array xdata, np.array ydata)\nfigure                  a matplotlib.figure.Figure instance\nlabel                   any string\nlinestyle or ls         [ ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'steps'`` | ...]\nlinewidth or lw         float value in points\nmarker                  [ ``'+'`` | ``','`` | ``'.'`` | ``'1'`` | ``'2'`` | ``'3'`` | ``'4'`` ]\nmarkeredgecolor or mec  any matplotlib color\nmarkeredgewidth or mew  float value in points\nmarkerfacecolor or mfc  any matplotlib color\nmarkersize or ms        float\nmarkevery               [ None | integer | (startind, stride) ]\npicker                  used in interactive line selection\npickradius              the line pick selection radius\nsolid_capstyle          [``'butt'`` | ``'round'`` | ``'projecting'``]\nsolid_joinstyle         [``'miter'`` | ``'round'`` | ``'bevel'``]\ntransform               a matplotlib.transforms.Transform instance\nvisible                 [True | False]\nxdata                   np.array\nydata                   np.array\nzorder                  any number\n======================  ==================================================\n\nTo get a list of settable line properties, call the\n`~.pyplot.setp` function with a line or lines as argument\n\n.. sourcecode:: ipython\n\n    In [69]: lines = plt.plot([1, 2, 3])\n\n    In [70]: plt.setp(lines)\n      alpha: float\n      animated: [True | False]\n      antialiased or aa: [True | False]\n      ...snip\n\n\n\n## Working with multiple figures and Axes\n\nMATLAB, and :mod:`.pyplot`, have the concept of the current figure\nand the current Axes.  All plotting functions apply to the current\nAxes.  The function `~.pyplot.gca` returns the current Axes (a\n`matplotlib.axes.Axes` instance), and `~.pyplot.gcf` returns the current\nfigure (a `matplotlib.figure.Figure` instance). Normally, you don't have to\nworry about this, because it is all taken care of behind the scenes.  Below\nis a script to create two subplots.\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:54.880178Z",
     "start_time": "2024-08-07T07:40:54.778702Z"
    }
   },
   "source": [
    "def f(t):\n    return np.exp(-t) * np.cos(2*np.pi*t)\n\nt1 = np.arange(0.0, 5.0, 0.1)\nt2 = np.arange(0.0, 5.0, 0.02)\n\nplt.figure()\nplt.subplot(211)\nplt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n\nplt.subplot(212)\nplt.plot(t2, np.cos(2*np.pi*t2), 'r--')\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `~.pyplot.figure` call here is optional because a figure will be created\nif none exists, just as an Axes will be created (equivalent to an explicit\n``subplot()`` call) if none exists.\nThe `~.pyplot.subplot` call specifies ``numrows,\nnumcols, plot_number`` where ``plot_number`` ranges from 1 to\n``numrows*numcols``.  The commas in the ``subplot`` call are\noptional if ``numrows*numcols<10``.  So ``subplot(211)`` is identical\nto ``subplot(2, 1, 1)``.\n\nYou can create an arbitrary number of subplots\nand Axes.  If you want to place an Axes manually, i.e., not on a\nrectangular grid, use `~.pyplot.axes`,\nwhich allows you to specify the location as ``axes([left, bottom,\nwidth, height])`` where all values are in fractional (0 to 1)\ncoordinates.  See :doc:`/gallery/subplots_axes_and_figures/axes_demo` for an example of\nplacing Axes manually and :doc:`/gallery/subplots_axes_and_figures/subplot` for an\nexample with lots of subplots.\n\nYou can create multiple figures by using multiple\n`~.pyplot.figure` calls with an increasing figure\nnumber.  Of course, each figure can contain as many Axes and subplots\nas your heart desires::\n\n    import matplotlib.pyplot as plt\n    plt.figure(1)                # the first figure\n    plt.subplot(211)             # the first subplot in the first figure\n    plt.plot([1, 2, 3])\n    plt.subplot(212)             # the second subplot in the first figure\n    plt.plot([4, 5, 6])\n\n\n    plt.figure(2)                # a second figure\n    plt.plot([4, 5, 6])          # creates a subplot() by default\n\n    plt.figure(1)                # first figure current;\n                                 # subplot(212) still current\n    plt.subplot(211)             # make subplot(211) in the first figure\n                                 # current\n    plt.title('Easy as 1, 2, 3') # subplot 211 title\n\nYou can clear the current figure with `~.pyplot.clf`\nand the current Axes with `~.pyplot.cla`.  If you find\nit annoying that states (specifically the current image, figure and Axes)\nare being maintained for you behind the scenes, don't despair: this is just a thin\nstateful wrapper around an object-oriented API, which you can use\ninstead (see `artists_tutorial`)\n\nIf you are making lots of figures, you need to be aware of one\nmore thing: the memory required for a figure is not completely\nreleased until the figure is explicitly closed with\n`~.pyplot.close`.  Deleting all references to the\nfigure, and/or using the window manager to kill the window in which\nthe figure appears on the screen, is not enough, because pyplot\nmaintains internal references until `~.pyplot.close`\nis called.\n\n\n## Working with text\n\n`~.pyplot.text` can be used to add text in an arbitrary location, and\n`~.pyplot.xlabel`, `~.pyplot.ylabel` and `~.pyplot.title` are used to add\ntext in the indicated locations (see `text_intro` for a\nmore detailed example)\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:55.048944Z",
     "start_time": "2024-08-07T07:40:54.888893Z"
    }
   },
   "source": [
    "mu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\n\n# the histogram of the data\nn, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)\n\n\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\nplt.title('Histogram of IQ')\nplt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\nplt.axis([40, 160, 0, 0.03])\nplt.grid(True)\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the `~.pyplot.text` functions return a `matplotlib.text.Text`\ninstance.  Just as with lines above, you can customize the properties by\npassing keyword arguments into the text functions or using `~.pyplot.setp`::\n\n  t = plt.xlabel('my data', fontsize=14, color='red')\n\nThese properties are covered in more detail in `text_props`.\n\n\n### Using mathematical expressions in text\n\nMatplotlib accepts TeX equation expressions in any text expression.\nFor example to write the expression $\\sigma_i=15$ in the title,\nyou can write a TeX expression surrounded by dollar signs::\n\n    plt.title(r'$\\sigma_i=15$')\n\nThe ``r`` preceding the title string is important -- it signifies\nthat the string is a *raw* string and not to treat backslashes as\npython escapes.  matplotlib has a built-in TeX expression parser and\nlayout engine, and ships its own math fonts -- for details see\n`mathtext`.  Thus, you can use mathematical text across\nplatforms without requiring a TeX installation.  For those who have LaTeX\nand dvipng installed, you can also use LaTeX to format your text and\nincorporate the output directly into your display figures or saved\npostscript -- see `usetex`.\n\n\n### Annotating text\n\nThe uses of the basic `~.pyplot.text` function above\nplace text at an arbitrary position on the Axes.  A common use for\ntext is to annotate some feature of the plot, and the\n`~.pyplot.annotate` method provides helper\nfunctionality to make annotations easy.  In an annotation, there are\ntwo points to consider: the location being annotated represented by\nthe argument ``xy`` and the location of the text ``xytext``.  Both of\nthese arguments are ``(x, y)`` tuples.\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:55.134783Z",
     "start_time": "2024-08-07T07:40:55.058596Z"
    }
   },
   "source": [
    "ax = plt.subplot()\n\nt = np.arange(0.0, 5.0, 0.01)\ns = np.cos(2*np.pi*t)\nline, = plt.plot(t, s, lw=2)\n\nplt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n             arrowprops=dict(facecolor='black', shrink=0.05),\n             )\n\nplt.ylim(-2, 2)\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this basic example, both the ``xy`` (arrow tip) and ``xytext``\nlocations (text location) are in data coordinates.  There are a\nvariety of other coordinate systems one can choose -- see\n`annotations-tutorial` and `plotting-guide-annotation` for\ndetails.  More examples can be found in\n:doc:`/gallery/text_labels_and_annotations/annotation_demo`.\n\n\n## Logarithmic and other nonlinear axes\n\n:mod:`matplotlib.pyplot` supports not only linear axis scales, but also\nlogarithmic and logit scales. This is commonly used if data spans many orders\nof magnitude. Changing the scale of an axis is easy::\n\n    plt.xscale('log')\n\nAn example of four plots with the same data and different scales for the y-axis\nis shown below.\n\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T07:40:55.673888Z",
     "start_time": "2024-08-07T07:40:55.144268Z"
    }
   },
   "source": [
    "# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n# make up some data in the open interval (0, 1)\ny = np.random.normal(loc=0.5, scale=0.4, size=1000)\ny = y[(y > 0) & (y < 1)]\ny.sort()\nx = np.arange(len(y))\n\n# plot with various axes scales\nplt.figure()\n\n# linear\nplt.subplot(221)\nplt.plot(x, y)\nplt.yscale('linear')\nplt.title('linear')\nplt.grid(True)\n\n# log\nplt.subplot(222)\nplt.plot(x, y)\nplt.yscale('log')\nplt.title('log')\nplt.grid(True)\n\n# symmetric log\nplt.subplot(223)\nplt.plot(x, y - y.mean())\nplt.yscale('symlog', linthresh=0.01)\nplt.title('symlog')\nplt.grid(True)\n\n# logit\nplt.subplot(224)\nplt.plot(x, y)\nplt.yscale('logit')\nplt.title('logit')\nplt.grid(True)\n# Adjust the subplot layout, because the logit one may take more space\n# than usual, due to y-tick labels like \"1 - 10^{-3}\"\nplt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,\n                    wspace=0.35)\n\nplt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to add your own scale, see `matplotlib.scale` for\ndetails.\n\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
